Biodiversity Observatory Automation
A brief overview
The Biodiversity Observatory Automation Working Group aims to review and update the requirements for effective biodiversity assessment at a time of unprecedented environmental change and biodiversity loss. Biodiversity assessment faces several challenges, including time-intensive fieldwork, demanding post-fieldwork data processing and limited storage capacity. However, advances in the automation of data collection, increasing computing power and the integration of artificial intelligence offer promising solutions.
The first international meeting (https://www.lifewatch.eu/thematic-services-working-groups/biodiversity-observatory-automation) on this topic took place in Slovenia in April 2024. It brought together experts to share key achievements, address obstacles in monitoring and observational methods and discuss the needs and concerns of the various stakeholders.
The working group will explore modern approaches to biodiversity monitoring and detection, ranging from aerial observations to eDNA analysis. Efforts will focus on optimising data collection, improving data curation and exploration, using artificial intelligence and applying FAIR data principles to enable the creation of digital twins.
Through these initiatives, the group aims to improve biodiversity monitoring methods and close current gaps in order to find effective answers to global environmental challenges.
Shape the future of biodiversity assessment with us! Stay engaged with the Biodiversity Observatory Automation Working Group as we explore innovative technologies – from AI-powered analysis to automated data collection – that are transforming biodiversity monitoring. Engage with scientists and experts, access valuable resources and contribute to innovative solutions that improve our understanding of ecosystems in a rapidly changing world.
Need help joining? Check our FAQs
Key Objectives
The aim of the working group “Automating the Biodiversity Observatory” is to review and update the requirements for effective biodiversity assessment at a time of unprecedented environmental change and biodiversity loss. We will:
- Identify and integrate cutting-edge technologies, such as machine learning, remote sensing, and eDNA analysis, to improve biodiversity assessment.
- Reduce reliance on labor-intensive fieldwork by promoting automation.
- Develop efficient and scalable methods for biodiversity data collection and curation.
- Improve data storage, accessibility, and interoperability to support global research efforts.
- Leverage machine learning and computational models for species identification, habitat mapping, and ecological trend analysis.
- Automate data interpretation to enhance monitoring accuracy and efficiency.
- Ensure that biodiversity data is Findable, Accessible, Interoperable, and Reusable (FAIR).
- Develop standardized data-sharing protocols for improved collaboration.
- Strengthen global partnerships by organising conferences, workshops, and networking events.
- Share best practices and insights to create a unified approach to automated biodiversity assessment.
- Create virtual models of ecosystems to simulate environmental changes and predict biodiversity responses.
- Use simulation scenarios for planning and impact assessments in conservation efforts.
Photo by Bitnik Gao on Pexels
